Methods for Handling Multiple Outcomes in Health Data

Sivakamakshi Muthu Kumarasamy1, Shivadarshini Sreekanth1

1 School of Mathematical and Statistical Sciences, University of Galway

Background and Problem

Health research often involves multiple outcomes (e.g., survival, disease progression)1.

Current Methods

  • Analyse outcomes separately: Treats outcomes as independent and potentially miss biological and temporal links.
  • Composite endpoints: May hide heterogeneity in clinical importance, frequency, and treatment response. Losing predictive performance and clinical interpretability.

Emerging Methods

  • Clinical trials: Win-ratio preserves the hierarchy of endpoints importance while combining the analysis of the affect2.
  • Prediction: Multi-state models can be used to model a process where subjects transition from one state to the next3. And multi-task models are deep learning models which use common pathways to predict multi outcomes of interest4.

Objectives of Project

Compare strategies for both clinical trial design and prediction modelling that treat outcomes independently, as composite endpoints, and via emerging methods.

Data Source

  • SECOMBIT Trial: Phase II randomised; N ≈ 209 metastatic melanoma; 3 arms5,6.

  • Endpoints: Overall Survival (OS) and Progression Free Survival (PFS).

  • Covariates: Sites, Lactate Dehydrogenase (LDH), Tumour Mutational Burden (TMB), a genetic biomarker (JAK).

Baseline Summary Table (by Arm)

A
(N=69)
B
(N=69)
C
(N=68)
Sites
1 - 2 43 (62.3%) 40 (58.0%) 42 (61.8%)
>= 3 26 (37.7%) 29 (42.0%) 26 (38.2%)
LDH
Normal 41 (59.4%) 39 (56.5%) 47 (69.1%)
Elevated 26 (37.7%) 28 (40.6%) 20 (29.4%)
Missing 2 (2.9%) 2 (2.9%) 1 (1.5%)
TMB
< 10 20 (29.0%) 17 (24.6%) 18 (26.5%)
>= 10 8 (11.6%) 8 (11.6%) 12 (17.6%)
Missing 41 (59.4%) 44 (63.8%) 38 (55.9%)
JAK
Wild Type (Normal) 24 (34.8%) 17 (24.6%) 16 (23.5%)
Mutated 5 (7.2%) 7 (10.1%) 14 (20.6%)
Missing 40 (58.0%) 45 (65.2%) 38 (55.9%)

Preliminary Analysis

Time-to-event analyses was conducted for Overall Survival (OS) and Progression-Free Survival (PFS) separately. Survival distributions are visualised using Kaplan–Meier plots. Cox proportional hazards models are fitted to estimate covariate-adjusted effects, and as a preliminary predictive modelling strategy.

Kaplan - Meier Plot

Log-rank tests

Endpoint Chi.square df p.value
OS 2.80 2 0.2460
PFS 7.74 2 0.0208
Cox model — Overall Survival (OS)
Variable HR (95% CI) p-value
ArmB 0.98 (0.38–2.50) 0.9590
ArmC 0.83 (0.33–2.07) 0.6900
Sites>= 3 2.02 (0.93–4.42) 0.0764
LDHElevated 0.96 (0.43–2.15) 0.9130
TMB>= 10 0.72 (0.31–1.67) 0.4480
JAKMutated 0.63 (0.24–1.63) 0.3380

Cox model — Progression-Free Survival (PFS)
Variable HR (95% CI) p-value
ArmB 0.73 (0.30–1.77) 0.4870
ArmC 1.03 (0.48–2.22) 0.9470
Sites>= 3 1.60 (0.80–3.20) 0.1870
LDHElevated 1.14 (0.56–2.34) 0.7150
TMB>= 10 0.85 (0.40–1.78) 0.6660
JAKMutated 0.41 (0.16–1.03) 0.0571

Proportial Hazard Test

Endpoint PH.test.p.value
OS 0.0581
PFS 0.0119

Future Work

  • Compare independent outcome assessment with composite outcome approach
  • Assess the potential of hierarchical endpoint analysis such as the Win-Ratio
  • Explore multi-state methods and multi-task learning approaches for multi-endpoint risk prediction
  • Apply methods to more complex dataset

GitHub

The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/darshu-d/MSc-Research-project-

References

    1. Neuhäuser, M. (2006). How to deal with multiple endpoints in clinical trials. Fundamental & clinical pharmacology, 20(6), 515-523.
    2. Pocock, S. J., et al. (2012). The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European heart journal, 33(2), 176-182.
    3. Jackson, C. (2011). Multi-state models for panel data: the msm package for R. Journal of statistical software, 38, 1-28.
    4. Wang, L., et al. (2017, November). Multi-task survival analysis. In 2017 IEEE international conference on data mining (ICDM) (pp. 485-494). IEEE.
    5. Ascierto, P. A., et al. (2024). Sequential immunotherapy and targeted therapy for metastatic BRAF V600 mutated melanoma: 4-year survival and biomarkers evaluation from the phase II SECOMBIT trial. Nature Communications, 15(1), 146.
    6. Ascierto, P. A., et al. (2023). Sequencing of ipilimumab plus nivolumab and encorafenib plus binimetinib for untreated BRAF-mutated metastatic melanoma (SECOMBIT): a randomized, three-arm, open-label phase II trial. Journal of Clinical Oncology, 41(2), 212-221.